Ingestion: event-driven and CDC streams, Flink, batch, and file feeds to lake
The lake/lakehouse: Parquet, Trino, table formats, partitioning, modeling
The serving/cache tier: powering customer-facing screeners and analytics at production latency
Data sharing/feeds: Snowflake/Databricks (Delta Sharing), APIs, and file feeds
The reverse-ETL platform: pushing derived data back to operational systems
Data contracts/governance: schema governance, entitlements, data classification

🎯 Requirements

Built and operated a customer-facing data product under an SLA
Deep experience moving high-volume data in production: event-driven ingestion, CDC, Flink, batch
Strong on schema registries and contracts (Avro/Protobuf, evolution)
Fluent in Parquet, Trino, Kafka/Flink, Snowflake/Databricks
Product/governance instincts: multi-tenancy, entitlements, usage metering
Bonus: financial data or capital markets experience (Bloomberg/S&P/9fin)

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